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Abstract：
This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer\'s calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO2, and O3 sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas.
摘要：
本文解决了校准用于空气质量监测的低成本电化学传感器系统的挑战。污染物在大气中的扩散需要有效的监测系统，和低成本传感器提供了一个有前途的解决方案。然而,漂移等问题，交叉敏感性,和单位间的一致性引起了人们对其准确性和可靠性的担忧。该研究探讨了以下三种将传感器信号转换为浓度测量的校准方法：利用制造商提供的方程式，结合机器学习(ML)算法，并直接将ML应用于电压信号。在希腊的三个城市地点进行了实验。高端仪器为模型的训练和评估提供了参考浓度。结果表明，利用电压信号代替制造商的校准方程可以减少相同传感器之间的可变性。此外，后一种方法提高了CO的校准效率，NO,NO2和O3传感器，同时在ML算法中结合来自所有传感器的电压信号，利用交叉灵敏度提高校准性能。随机森林ML算法是校准城市地区使用的类似设备的有前途的解决方案。
